DocumentCode :
2553553
Title :
A graph based transductive ranking algorithm
Author :
Pan, Zhibin ; Wei, Xiaoyan
Author_Institution :
Coll. of Sci., Huazhong Agric. Univ., Wuhan, China
fYear :
2012
fDate :
29-31 May 2012
Firstpage :
991
Lastpage :
994
Abstract :
Semi-supervised ranking is a newly developed machine learning problem. In this paper, based on the graph constructed on both labeled and unlabeled data points, we propose a novel semi-supervised ranking algorithm in the transductive setting via a semi-supervised regression model. We also derive the solution in an explicit form for this model. Experiments on two QSAR data sets demonstrate its utility and effectiveness.
Keywords :
QSAR; graph theory; learning (artificial intelligence); regression analysis; QSAR data sets; graph construction; graph-based transductive ranking algorithm; labeled data points; machine learning problem; semisupervised ranking; semisupervised regression model; transdutive setting; unlabeled data points; Algorithm design and analysis; Biology; Compounds; Correlation; Laplace equations; Machine learning; Standards; Graph Laplacian; Quantitative Structure-Activity Relationship; Ranking; Semi-supervised learning;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Fuzzy Systems and Knowledge Discovery (FSKD), 2012 9th International Conference on
Conference_Location :
Sichuan
Print_ISBN :
978-1-4673-0025-4
Type :
conf
DOI :
10.1109/FSKD.2012.6234360
Filename :
6234360
Link To Document :
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